LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

LLM fine-tuning adapts a pretrained model to a specific task or domain; current tooling ranges from from-scratch training guides to efficient local adapters to automated synthetic data pipelines that can beat larger models at a fraction of the cost.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

Open source spans infrastructure, tooling, security risk, and platform trust — the cited sources collectively show it as a foundation for local AI, developer tooling, and code forges, with its benefits shadowed by real supply-chain and stewardship threats.

Software engineering spans craft, process, and judgment — how code is structured, tested, reviewed, deployed, and maintained — and the sources collected here collectively interrogate each layer as AI tooling reshapes who does what and why.